Modeling and evaluation of knowledge discovery in wholesale and retail industry

Abstract

This thesis demonstrates an enterprise-wide Knowledge Discovery in Databases (KDD) process CRISP for wholesale and retail industry, which can facilitate business decision-making processes and improve corporate profits. While part of the KDD process described here is well documented, the modeling and evaluations used in the commercial products is not reported in literature. Hence, the focus of this thesis is on the development and evaluation of models used in the knowledge discovery. Description of the underlying models will help the decision makers better understand the quality and limitations of the KDD process.

The usefulness of KDD process CRISP is illustrated for two companies, i.e. a multinational retailer and a small chain of specialty grocery stores. The detailed steps highlight business understanding, data exploration, data preparation. data modeling, results evaluation, and interpretation. The methodologies applied in this thesis include prediction, clustering and association to discover knowledge about products/suppliers, consumers, and business units.